Energetic landscapes and fuzzy complexes: Computational targeting of viral intrinsically disordered proteins

The increased recognition of intrinsically disordered proteins (IDPs) as critical mediators in viral infections has shifted attention toward fuzzy drug targets, challenging the conventional structure-based paradigms of drug discovery due to their inherent conformational flexibility. The binding of viral IDPs and IDRs to host factors occurs in dynamic, multivalent, and context-dependent interactions and constitute flexible complexes, which form the basis of pathogenicity and immune evasion. In this review, the disordered proteins of viruses are considered with a combination of molecular, computational, and translational insights to assess the next-generation antiviral targets. In this, we have discuss about the energetic landscapes that control the disorder, functional disorder order transitions and the fuzzy interfaces as centers of the networks of virus-host interactions. Special focus is kept on the new lines of computational and AI-directed technologies such as ensemble-based docking, machine-learning computational models of IDP ligand recognition, and multi-omics-driven target prioritization. The experimental approaches that are modified to characterize disordered systems, including NMR spectroscopy and hybrid structural biology, are also reviewed. The translational applicability of the targeting of viral fuzziness is highlighted by case studies of HIV-1, influenza, SARS-CoV-2, and emerging viral pathogens. We also provide directions about the future involving adaptive pharmacophores, customized antiviral approaches, and AI-driven ensemble targeting making disordered viral proteins a paradigm shift in antiviral drug discovery.